Structured Streaming provides a fast, fault-tolerant and exactly-once stream processing while allowing users to use DataFrame/Dataset API to express streaming aggregations, EVENT time windows, etc. 

The computation is executed on the same Spark SQL engine. You express your streaming computation the same way you would express a BATCH computation using DataFrame/Dataset. Spark SQL engine takes care of running it incrementally and updating the final result as and when streaming data KEEPS arriving.

Output modes define the way data is written to result from the table. There are three different output modes in Spark Structured Streaming.

• Append: In this mode, only news rows are written to sink. This mode is suited when the output table stores immutable result.

• Complete: In this mode, all the rows are written to sink EVERY time. This mode should be used when aggregations need to be applied to input data.

• Update: In this mode, only updated records are written to output sink, UNLIKE earlier mode in which all the records were written to sink.